CUSUM learning curves: what they can and can't tell us.

Introduction: There has been increased interest in assessing the surgeon learning curve for new skill acquisition. While there is no consensus around the best methodology, one of the most frequently used learning curve assessments in the surgical literature is the cumulative sum curve (CUSUM) of operative time. To demonstrate the limitations of this methodology, we assessed the CUSUM of console time across cohorts of surgeons with differing case acquisition rates while varying the total number of cases used to calculate the CUSUM.

A combined association test for rare variants using family and case-control data.

Statistical association tests for rare variants can be classified as the burden approach and the sequence kernel association test (SKAT) approach. The burden and SKAT approaches, originally developed for case-control analysis, have also been extended to family-based tests. In the presence of both case-control and family data for a study, joint analysis for the combined data set can increase the statistical power.

Pathway Analysis Incorporating Protein-Protein Interaction Networks Identified Candidate Pathways for the Seven Common Diseases.

Pathway analysis has become popular as a secondary analysis strategy for genome-wide association studies (GWAS). Most of the current pathway analysis methods aggregate signals from the main effects of single nucleotide polymorphisms (SNPs) in genes within a pathway without considering the effects of gene-gene interactions. However, gene-gene interactions can also have critical effects on complex diseases.

A multi-SNP association test for complex diseases incorporating an optimal P-value threshold algorithm in nuclear families.

Background: Genome-wide association studies (GWAS) have become a common approach to identifying single nucleotide polymorphisms (SNPs) associated with complex diseases. As complex diseases are caused by the joint effects of multiple genes, while the effect of individual gene or SNP is modest, a method considering the joint effects of multiple SNPs can be more powerful than testing individual SNPs. The multi-SNP analysis aims to test association based on a SNP set, usually defined based on biological knowledge such as gene or pathway, which may contain only a portion of SNPs with effects on the disease.